library(tidyverse)
library(plotly)
l_df <- read_csv("levels-cleaned.csv")
Missing column names filled in: 'X1' [1]Parsed with column specification:
cols(
.default = col_double(),
timestamp = col_character(),
company = col_character(),
level = col_character(),
title = col_character(),
location = col_character(),
tag = col_character(),
gender = col_character(),
city = col_character(),
state = col_character(),
month = col_character()
)
See spec(...) for full column specifications.
companyCompensation <-
l_df %>%
group_by(year, company) %>%
summarise(yearly_compensation = mean(totalyearlycompensation))
`summarise()` regrouping output by 'year' (override with `.groups` argument)
cityCompensation <-
l_df %>%
group_by(year, location) %>%
summarise(yearly_compensation = mean(totalyearlycompensation))
`summarise()` regrouping output by 'year' (override with `.groups` argument)
Yearly mean compensation for top 5 companies in 2017, 2018, 2019, 2020, 2021
companyCompensation2017 <-
companyCompensation %>%
filter(year == 2017) %>%
arrange(desc(yearly_compensation)) %>%
head(n = 5) %>%
plot_ly(
x = ~yearly_compensation,
y = ~company,
type = "bar",
text = ~paste(company, ": $", round(yearly_compensation), sep = ""),
hoverinfo = "text"
) %>%
layout(showlegend = FALSE)
companyCompensation2018 <-
companyCompensation %>%
filter(year == 2018) %>%
arrange(desc(yearly_compensation)) %>%
head(n = 5) %>%
plot_ly(
x = ~yearly_compensation,
y = ~company,
type = "bar",
text = ~paste(company, ": $", round(yearly_compensation), sep = ""),
hoverinfo = "text"
) %>%
layout(showlegend = FALSE)
companyCompensation2019 <-
companyCompensation %>%
filter(year == 2019) %>%
arrange(desc(yearly_compensation)) %>%
head(n = 5) %>%
plot_ly(
x = ~yearly_compensation,
y = ~company,
type = "bar",
text = ~paste(company, ": $", round(yearly_compensation), sep = ""),
hoverinfo = "text"
) %>%
layout(showlegend = FALSE)
companyCompensation2020 <-
companyCompensation %>%
filter(year == 2020) %>%
arrange(desc(yearly_compensation)) %>%
head(n = 5) %>%
plot_ly(
x = ~yearly_compensation,
y = ~company,
type = "bar",
text = ~paste(company, ": $", round(yearly_compensation), sep = ""),
hoverinfo = "text"
) %>%
layout(showlegend = FALSE)
companyCompensation2021 <-
companyCompensation %>%
filter(year == 2021) %>%
arrange(desc(yearly_compensation)) %>%
head(n = 5) %>%
plot_ly(
x = ~yearly_compensation,
y = ~company,
type = "bar",
text = ~paste(company, ": $", round(yearly_compensation), sep = ""),
hoverinfo = "text"
) %>%
layout(showlegend = FALSE)
subplot(companyCompensation2017, companyCompensation2018, companyCompensation2019, companyCompensation2020, companyCompensation2021, nrows = 5)
Yearly mean compensation for top 5 cities in 2017, 2018, 2019, 2020, 2021
cityCompensation2017 <-
cityCompensation %>%
filter(year == 2017) %>%
arrange(desc(yearly_compensation)) %>%
head(n = 5) %>%
plot_ly(
x = ~yearly_compensation,
y = ~location,
type = "bar",
text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
hoverinfo = "text"
) %>%
layout(showlegend = FALSE)
cityCompensation2018 <-
cityCompensation %>%
filter(year == 2018) %>%
arrange(desc(yearly_compensation)) %>%
head(n = 5) %>%
plot_ly(
x = ~yearly_compensation,
y = ~location,
type = "bar",
text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
hoverinfo = "text"
) %>%
layout(showlegend = FALSE)
cityCompensation2019 <-
cityCompensation %>%
filter(year == 2019) %>%
arrange(desc(yearly_compensation)) %>%
head(n = 5) %>%
plot_ly(
x = ~yearly_compensation,
y = ~location,
type = "bar",
text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
hoverinfo = "text"
) %>%
layout(showlegend = FALSE)
cityCompensation2020 <-
cityCompensation %>%
filter(year == 2020) %>%
arrange(desc(yearly_compensation)) %>%
head(n = 5) %>%
plot_ly(
x = ~yearly_compensation,
y = ~location,
type = "bar",
text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
hoverinfo = "text"
) %>%
layout(showlegend = FALSE)
cityCompensation2021 <-
cityCompensation %>%
filter(year == 2021) %>%
arrange(desc(yearly_compensation)) %>%
head(n = 5) %>%
plot_ly(
x = ~yearly_compensation,
y = ~location,
type = "bar",
text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
hoverinfo = "text"
) %>%
layout(showlegend = FALSE)
subplot(cityCompensation2017, cityCompensation2018, cityCompensation2019, cityCompensation2020, cityCompensation2021, nrows = 5)
Yearly mean compensation for last 5 companies in 2017, 2018, 2019, 2020, 2021
companyCompensation2017 <-
companyCompensation %>%
filter(year == 2017) %>%
arrange(yearly_compensation) %>%
head(n = 5) %>%
plot_ly(
x = ~yearly_compensation,
y = ~company,
type = "bar",
text = ~paste(company, ": $", round(yearly_compensation), sep = ""),
hoverinfo = "text"
) %>%
layout(showlegend = FALSE)
companyCompensation2018 <-
companyCompensation %>%
filter(year == 2018) %>%
arrange(yearly_compensation) %>%
head(n = 5) %>%
plot_ly(
x = ~yearly_compensation,
y = ~company,
type = "bar",
text = ~paste(company, ": $", round(yearly_compensation), sep = ""),
hoverinfo = "text"
) %>%
layout(showlegend = FALSE)
companyCompensation2019 <-
companyCompensation %>%
filter(year == 2019) %>%
arrange(yearly_compensation) %>%
head(n = 5) %>%
plot_ly(
x = ~yearly_compensation,
y = ~company,
type = "bar",
text = ~paste(company, ": $", round(yearly_compensation), sep = ""),
hoverinfo = "text"
) %>%
layout(showlegend = FALSE)
companyCompensation2020 <-
companyCompensation %>%
filter(year == 2020) %>%
arrange(yearly_compensation) %>%
head(n = 5) %>%
plot_ly(
x = ~yearly_compensation,
y = ~company,
type = "bar",
text = ~paste(company, ": $", round(yearly_compensation), sep = ""),
hoverinfo = "text"
) %>%
layout(showlegend = FALSE)
companyCompensation2021 <-
companyCompensation %>%
filter(year == 2021) %>%
arrange(yearly_compensation) %>%
head(n = 5) %>%
plot_ly(
x = ~yearly_compensation,
y = ~company,
type = "bar",
text = ~paste(company, ": $", round(yearly_compensation), sep = ""),
hoverinfo = "text"
) %>%
layout(showlegend = FALSE)
subplot(companyCompensation2017, companyCompensation2018, companyCompensation2019, companyCompensation2020, companyCompensation2021, nrows = 5)
Yearly mean compensation for last 5 cities in 2017, 2018, 2019, 2020, 2021
cityCompensation2017 <-
cityCompensation %>%
filter(year == 2017) %>%
arrange(yearly_compensation) %>%
head(n = 5) %>%
plot_ly(
x = ~yearly_compensation,
y = ~location,
type = "bar",
text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
hoverinfo = "text"
) %>%
layout(showlegend = FALSE)
cityCompensation2018 <-
cityCompensation %>%
filter(year == 2018) %>%
arrange(yearly_compensation) %>%
head(n = 5) %>%
plot_ly(
x = ~yearly_compensation,
y = ~location,
type = "bar",
text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
hoverinfo = "text"
) %>%
layout(showlegend = FALSE)
cityCompensation2019 <-
cityCompensation %>%
filter(year == 2019) %>%
arrange(yearly_compensation) %>%
head(n = 5) %>%
plot_ly(
x = ~yearly_compensation,
y = ~location,
type = "bar",
text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
hoverinfo = "text"
) %>%
layout(showlegend = FALSE)
cityCompensation2020 <-
cityCompensation %>%
filter(year == 2020) %>%
arrange(yearly_compensation) %>%
head(n = 5) %>%
plot_ly(
x = ~yearly_compensation,
y = ~location,
type = "bar",
text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
hoverinfo = "text"
) %>%
layout(showlegend = FALSE)
cityCompensation2021 <-
cityCompensation %>%
filter(year == 2021) %>%
arrange(yearly_compensation) %>%
head(n = 5) %>%
plot_ly(
x = ~yearly_compensation,
y = ~location,
type = "bar",
text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
hoverinfo = "text"
) %>%
layout(showlegend = FALSE)
subplot(cityCompensation2017, cityCompensation2018, cityCompensation2019, cityCompensation2020, cityCompensation2021, nrows = 5)
Making YOE Levels
YOE_Levels <-
l_df %>%
mutate(YOE = case_when(between(yearsofexperience, 0, 2) ~ "0-2",
between(yearsofexperience, 2, 5) ~ "2-5",
between(yearsofexperience, 5, 10) ~ "5-10",
yearsofexperience >= 10 ~ "10+"))
cityCompensationYOE <-
YOE_Levels %>%
group_by(year, YOE, location) %>%
summarise(yearly_compensation = mean(totalyearlycompensation))
`summarise()` regrouping output by 'year', 'YOE' (override with `.groups` argument)
2018 top 5 locations with experience plots
cityCompensation2018_01 <-
cityCompensationYOE %>%
filter(YOE == "0-2") %>%
filter(year == 2018) %>%
arrange(desc(yearly_compensation)) %>%
head(n = 5) %>%
plot_ly(
x = ~yearly_compensation,
y = ~location,
type = "bar",
text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
hoverinfo = "text"
) %>%
layout(showlegend = FALSE)
cityCompensation2018_15 <-
cityCompensationYOE %>%
filter(YOE == "2-5") %>%
filter(year == 2018) %>%
arrange(desc(yearly_compensation)) %>%
head(n = 5) %>%
plot_ly(
x = ~yearly_compensation,
y = ~location,
type = "bar",
text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
hoverinfo = "text"
) %>%
layout(showlegend = FALSE)
cityCompensation2018_510 <-
cityCompensationYOE %>%
filter(YOE == "5-10") %>%
filter(year == 2018) %>%
arrange(desc(yearly_compensation)) %>%
head(n = 5) %>%
plot_ly(
x = ~yearly_compensation,
y = ~location,
type = "bar",
text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
hoverinfo = "text"
) %>%
layout(showlegend = FALSE)
cityCompensation2018_10_ <-
cityCompensationYOE %>%
filter(YOE == "10+") %>%
filter(year == 2018) %>%
arrange(desc(yearly_compensation)) %>%
head(n = 5) %>%
plot_ly(
x = ~yearly_compensation,
y = ~location,
type = "bar",
text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
hoverinfo = "text"
) %>%
layout(showlegend = FALSE)
subplot(cityCompensation2018_01, cityCompensation2018_15, cityCompensation2018_510, cityCompensation2018_10_, nrows = 4)
2021 top 5 locations with experience plots
cityCompensation2021_01 <-
cityCompensationYOE %>%
filter(YOE == "0-2") %>%
filter(year == 2021) %>%
arrange(desc(yearly_compensation)) %>%
head(n = 5) %>%
plot_ly(
x = ~yearly_compensation,
y = ~location,
type = "bar",
text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
hoverinfo = "text"
) %>%
layout(showlegend = FALSE)
cityCompensation2021_15 <-
cityCompensationYOE %>%
filter(YOE == "2-5") %>%
filter(year == 2021) %>%
arrange(desc(yearly_compensation)) %>%
head(n = 5) %>%
plot_ly(
x = ~yearly_compensation,
y = ~location,
type = "bar",
text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
hoverinfo = "text"
) %>%
layout(showlegend = FALSE)
cityCompensation2021_510 <-
cityCompensationYOE %>%
filter(YOE == "5-10") %>%
filter(year == 2021) %>%
arrange(desc(yearly_compensation)) %>%
head(n = 5) %>%
plot_ly(
x = ~yearly_compensation,
y = ~location,
type = "bar",
text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
hoverinfo = "text"
) %>%
layout(showlegend = FALSE)
cityCompensation2021_10_ <-
cityCompensationYOE %>%
filter(YOE == "10+") %>%
filter(year == 2021) %>%
arrange(desc(yearly_compensation)) %>%
head(n = 5) %>%
plot_ly(
x = ~yearly_compensation,
y = ~location,
type = "bar",
text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
hoverinfo = "text"
) %>%
layout(showlegend = FALSE)
subplot(cityCompensation2021_01, cityCompensation2021_15, cityCompensation2021_510, cityCompensation2021_10_, nrows = 4)
2018 last 5 locations with experience plots
cityCompensation2018_01 <-
cityCompensationYOE %>%
filter(YOE == "0-2") %>%
filter(year == 2018) %>%
arrange(yearly_compensation) %>%
head(n = 5) %>%
plot_ly(
x = ~yearly_compensation,
y = ~location,
type = "bar",
text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
hoverinfo = "text"
) %>%
layout(showlegend = FALSE)
cityCompensation2018_15 <-
cityCompensationYOE %>%
filter(YOE == "2-5") %>%
filter(year == 2018) %>%
arrange(yearly_compensation) %>%
head(n = 5) %>%
plot_ly(
x = ~yearly_compensation,
y = ~location,
type = "bar",
text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
hoverinfo = "text"
) %>%
layout(showlegend = FALSE)
cityCompensation2018_510 <-
cityCompensationYOE %>%
filter(YOE == "5-10") %>%
filter(year == 2018) %>%
arrange(yearly_compensation) %>%
head(n = 5) %>%
plot_ly(
x = ~yearly_compensation,
y = ~location,
type = "bar",
text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
hoverinfo = "text"
) %>%
layout(showlegend = FALSE)
cityCompensation2018_10_ <-
cityCompensationYOE %>%
filter(YOE == "10+") %>%
filter(year == 2018) %>%
arrange(yearly_compensation) %>%
head(n = 5) %>%
plot_ly(
x = ~yearly_compensation,
y = ~location,
type = "bar",
text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
hoverinfo = "text"
) %>%
layout(showlegend = FALSE)
subplot(cityCompensation2018_01, cityCompensation2018_15, cityCompensation2018_510, cityCompensation2018_10_, nrows = 4)
2021 last 5 locations with experience plots
cityCompensation2021_01 <-
cityCompensationYOE %>%
filter(YOE == "0-2") %>%
filter(year == 2021) %>%
arrange(yearly_compensation) %>%
head(n = 5) %>%
plot_ly(
x = ~yearly_compensation,
y = ~location,
type = "bar",
text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
hoverinfo = "text"
) %>%
layout(showlegend = FALSE)
cityCompensation2021_15 <-
cityCompensationYOE %>%
filter(YOE == "2-5") %>%
filter(year == 2021) %>%
arrange(yearly_compensation) %>%
head(n = 5) %>%
plot_ly(
x = ~yearly_compensation,
y = ~location,
type = "bar",
text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
hoverinfo = "text"
) %>%
layout(showlegend = FALSE)
cityCompensation2021_510 <-
cityCompensationYOE %>%
filter(YOE == "5-10") %>%
filter(year == 2021) %>%
arrange(yearly_compensation) %>%
head(n = 5) %>%
plot_ly(
x = ~yearly_compensation,
y = ~location,
type = "bar",
text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
hoverinfo = "text"
) %>%
layout(showlegend = FALSE)
cityCompensation2021_10_ <-
cityCompensationYOE %>%
filter(YOE == "10+") %>%
filter(year == 2021) %>%
arrange(yearly_compensation) %>%
head(n = 5) %>%
plot_ly(
x = ~yearly_compensation,
y = ~location,
type = "bar",
text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
hoverinfo = "text"
) %>%
layout(showlegend = FALSE)
subplot(cityCompensation2021_01, cityCompensation2021_15, cityCompensation2021_510, cityCompensation2021_10_, nrows = 4)
---
title: "R Notebook"
output:
  html_document:
    df_print: paged
---

```{r}
library(tidyverse)
library(plotly)
```

```{r}
l_df <- read_csv("levels-cleaned.csv")
```

```{r}
companyCompensation <- 
  l_df %>%
  group_by(year, company) %>%
  summarise(yearly_compensation = mean(totalyearlycompensation))

cityCompensation <- 
  l_df %>%
  group_by(year, location) %>%
  summarise(yearly_compensation = mean(totalyearlycompensation))
```

Yearly mean compensation for top 5 companies in 2017, 2018, 2019, 2020, 2021
```{r}
companyCompensation2017 <- 
  companyCompensation %>%
  filter(year == 2017) %>%
  arrange(desc(yearly_compensation)) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~company,
    type = "bar",
    text = ~paste(company, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

companyCompensation2018 <- 
  companyCompensation %>%
  filter(year == 2018) %>%
  arrange(desc(yearly_compensation)) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~company,
    type = "bar",
    text = ~paste(company, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

companyCompensation2019 <- 
  companyCompensation %>%
  filter(year == 2019) %>%
  arrange(desc(yearly_compensation)) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~company,
    type = "bar",
    text = ~paste(company, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

companyCompensation2020 <- 
  companyCompensation %>%
  filter(year == 2020) %>%
  arrange(desc(yearly_compensation)) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~company,
    type = "bar",
    text = ~paste(company, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

companyCompensation2021 <- 
  companyCompensation %>%
  filter(year == 2021) %>%
  arrange(desc(yearly_compensation)) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~company,
    type = "bar",
    text = ~paste(company, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

subplot(companyCompensation2017, companyCompensation2018, companyCompensation2019, companyCompensation2020, companyCompensation2021, nrows = 5)
```

Yearly mean compensation for top 5 cities in 2017, 2018, 2019, 2020, 2021
```{r}
cityCompensation2017 <- 
  cityCompensation %>%
  filter(year == 2017) %>%
  arrange(desc(yearly_compensation)) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~location,
    type = "bar",
    text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

cityCompensation2018 <- 
  cityCompensation %>%
  filter(year == 2018) %>%
  arrange(desc(yearly_compensation)) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~location,
    type = "bar",
    text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

cityCompensation2019 <- 
  cityCompensation %>%
  filter(year == 2019) %>%
  arrange(desc(yearly_compensation)) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~location,
    type = "bar",
    text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

cityCompensation2020 <- 
  cityCompensation %>%
  filter(year == 2020) %>%
  arrange(desc(yearly_compensation)) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~location,
    type = "bar",
    text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

cityCompensation2021 <- 
  cityCompensation %>%
  filter(year == 2021) %>%
  arrange(desc(yearly_compensation)) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~location,
    type = "bar",
    text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

subplot(cityCompensation2017, cityCompensation2018, cityCompensation2019, cityCompensation2020, cityCompensation2021, nrows = 5)
```

Yearly mean compensation for last 5 companies in 2017, 2018, 2019, 2020, 2021
```{r}
companyCompensation2017 <- 
  companyCompensation %>%
  filter(year == 2017) %>%
  arrange(yearly_compensation) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~company,
    type = "bar",
    text = ~paste(company, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

companyCompensation2018 <- 
  companyCompensation %>%
  filter(year == 2018) %>%
  arrange(yearly_compensation) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~company,
    type = "bar",
    text = ~paste(company, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

companyCompensation2019 <- 
  companyCompensation %>%
  filter(year == 2019) %>%
  arrange(yearly_compensation) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~company,
    type = "bar",
    text = ~paste(company, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

companyCompensation2020 <- 
  companyCompensation %>%
  filter(year == 2020) %>%
  arrange(yearly_compensation) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~company,
    type = "bar",
    text = ~paste(company, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

companyCompensation2021 <- 
  companyCompensation %>%
  filter(year == 2021) %>%
  arrange(yearly_compensation) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~company,
    type = "bar",
    text = ~paste(company, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

subplot(companyCompensation2017, companyCompensation2018, companyCompensation2019, companyCompensation2020, companyCompensation2021, nrows = 5)
```

Yearly mean compensation for last 5 cities in 2017, 2018, 2019, 2020, 2021
```{r}
cityCompensation2017 <- 
  cityCompensation %>%
  filter(year == 2017) %>%
  arrange(yearly_compensation) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~location,
    type = "bar",
    text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

cityCompensation2018 <- 
  cityCompensation %>%
  filter(year == 2018) %>%
  arrange(yearly_compensation) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~location,
    type = "bar",
    text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

cityCompensation2019 <- 
  cityCompensation %>%
  filter(year == 2019) %>%
  arrange(yearly_compensation) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~location,
    type = "bar",
    text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

cityCompensation2020 <- 
  cityCompensation %>%
  filter(year == 2020) %>%
  arrange(yearly_compensation) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~location,
    type = "bar",
    text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

cityCompensation2021 <- 
  cityCompensation %>%
  filter(year == 2021) %>%
  arrange(yearly_compensation) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~location,
    type = "bar",
    text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

subplot(cityCompensation2017, cityCompensation2018, cityCompensation2019, cityCompensation2020, cityCompensation2021, nrows = 5)
```

Making YOE Levels
```{r}
YOE_Levels <- 
  l_df %>%
  mutate(YOE = case_when(between(yearsofexperience, 0, 2) ~ "0-2",
                         between(yearsofexperience, 2, 5) ~ "2-5",
                         between(yearsofexperience, 5, 10) ~ "5-10",
                         yearsofexperience >= 10 ~ "10+"))

cityCompensationYOE <-
  YOE_Levels %>%
  group_by(year, YOE, location) %>%
  summarise(yearly_compensation = mean(totalyearlycompensation))
```

2018 top 5 locations with experience plots
```{r}
cityCompensation2018_01 <- 
  cityCompensationYOE %>%
  filter(YOE == "0-2") %>%
  filter(year == 2018) %>%
  arrange(desc(yearly_compensation)) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~location,
    type = "bar",
    text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

cityCompensation2018_15 <- 
  cityCompensationYOE %>%
  filter(YOE == "2-5") %>%
  filter(year == 2018) %>%
  arrange(desc(yearly_compensation)) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~location,
    type = "bar",
    text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

cityCompensation2018_510 <- 
  cityCompensationYOE %>%
  filter(YOE == "5-10") %>%
  filter(year == 2018) %>%
  arrange(desc(yearly_compensation)) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~location,
    type = "bar",
    text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

cityCompensation2018_10_ <- 
  cityCompensationYOE %>%
  filter(YOE == "10+") %>%
  filter(year == 2018) %>%
  arrange(desc(yearly_compensation)) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~location,
    type = "bar",
    text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

subplot(cityCompensation2018_01, cityCompensation2018_15, cityCompensation2018_510, cityCompensation2018_10_, nrows = 4)
```

2021 top 5 locations with experience plots
```{r}
cityCompensation2021_01 <- 
  cityCompensationYOE %>%
  filter(YOE == "0-2") %>%
  filter(year == 2021) %>%
  arrange(desc(yearly_compensation)) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~location,
    type = "bar",
    text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

cityCompensation2021_15 <- 
  cityCompensationYOE %>%
  filter(YOE == "2-5") %>%
  filter(year == 2021) %>%
  arrange(desc(yearly_compensation)) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~location,
    type = "bar",
    text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

cityCompensation2021_510 <- 
  cityCompensationYOE %>%
  filter(YOE == "5-10") %>%
  filter(year == 2021) %>%
  arrange(desc(yearly_compensation)) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~location,
    type = "bar",
    text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

cityCompensation2021_10_ <- 
  cityCompensationYOE %>%
  filter(YOE == "10+") %>%
  filter(year == 2021) %>%
  arrange(desc(yearly_compensation)) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~location,
    type = "bar",
    text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

subplot(cityCompensation2021_01, cityCompensation2021_15, cityCompensation2021_510, cityCompensation2021_10_, nrows = 4)
```

2018 last 5 locations with experience plots
```{r}
cityCompensation2018_01 <- 
  cityCompensationYOE %>%
  filter(YOE == "0-2") %>%
  filter(year == 2018) %>%
  arrange(yearly_compensation) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~location,
    type = "bar",
    text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

cityCompensation2018_15 <- 
  cityCompensationYOE %>%
  filter(YOE == "2-5") %>%
  filter(year == 2018) %>%
  arrange(yearly_compensation) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~location,
    type = "bar",
    text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

cityCompensation2018_510 <- 
  cityCompensationYOE %>%
  filter(YOE == "5-10") %>%
  filter(year == 2018) %>%
  arrange(yearly_compensation) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~location,
    type = "bar",
    text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

cityCompensation2018_10_ <- 
  cityCompensationYOE %>%
  filter(YOE == "10+") %>%
  filter(year == 2018) %>%
  arrange(yearly_compensation) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~location,
    type = "bar",
    text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

subplot(cityCompensation2018_01, cityCompensation2018_15, cityCompensation2018_510, cityCompensation2018_10_, nrows = 4)
```

2021 last 5 locations with experience plots
```{r}
cityCompensation2021_01 <- 
  cityCompensationYOE %>%
  filter(YOE == "0-2") %>%
  filter(year == 2021) %>%
  arrange(yearly_compensation) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~location,
    type = "bar",
    text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

cityCompensation2021_15 <- 
  cityCompensationYOE %>%
  filter(YOE == "2-5") %>%
  filter(year == 2021) %>%
  arrange(yearly_compensation) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~location,
    type = "bar",
    text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

cityCompensation2021_510 <- 
  cityCompensationYOE %>%
  filter(YOE == "5-10") %>%
  filter(year == 2021) %>%
  arrange(yearly_compensation) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~location,
    type = "bar",
    text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

cityCompensation2021_10_ <- 
  cityCompensationYOE %>%
  filter(YOE == "10+") %>%
  filter(year == 2021) %>%
  arrange(yearly_compensation) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~location,
    type = "bar",
    text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

subplot(cityCompensation2021_01, cityCompensation2021_15, cityCompensation2021_510, cityCompensation2021_10_, nrows = 4)
```
